the way to calculate `time_means` in script get_stats.py is wrong

[nvlabs]

the way to calculate `time_means` in script get_stats.py is wrong

[YueZhou-oh]

hey, are training and test .h5 files , eg. train/2015.h5 with simliar data shape (4D data)

[phrasenmaeher]

I am also wondering about that, did you find any solution so far?
In their paper they write

we use a time-averaged climatology in this work, motivated by [Rasp et al., 2020])

which is https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002405 defined just above A1, so that seems to be the correct way 🤷🏼

[phrasenmaeher]

Digging further into this, I found in the appendix this description:

long-term-mean-subtracted value of predicted (/true) variable v at the location denoted by the grid co-ordinates (m, n) at the forecast time-step l. The long-term mean of a variable is simply the mean value of that variable over a large number of historical samples in the training dataset. The long-term mean-subtracted variables X ̃ pred/true represent the anomalies of those variables that are not captured by the long term mean values

which reads that we subtract from our variables their mean -- which we do during data loading, and the mean is correctly computed over a long term (in get_stats.py)

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Edit: However, there's the thing that the variables are also scaled by their std_dev. so it's not only the mean that is removed